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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights
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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities
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inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic
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. The successful candidate will engage in innovative research addressing statistical methodology, machine learning, and/or learning techniques in complex biomedical and health-related challenges. We particularly
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-class research and teaching environment with strong support for international collaboration. Learn more about staff benefits on our website . HOW TO APPLY Please submit your application, together
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The University of London The University of London is both the UK’s largest provider of international distance and online learning and the convenor of a federation of 17 renowned higher education
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The Leibniz Institute for Neurobiology (LIN) is an internationally recognized neuroscience research institute dedicated to understanding the neural mechanisms of learning and memory across
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professionals; generates knowledge and treatments; and cares for patients and communities with compassion and respect. We value excellence, inclusiveness, collaboration, and discovery. To learn more about the
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. Postdoctoral Associate - Multi-agentic Coordination, Self-learning and AI Safety in Health Be You. The Department of Surgery at Duke University Medical Center seeks a Postdoctoral Fellow to lead research in
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 18 minutes ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward